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     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "for dirname, _, filenames in os.walk('./train'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The path to the directory where the original\n",
    "# dataset was uncompressed\n",
    "original_dataset_dir = './train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The directory where we will\n",
    "# store our smaller dataset\n",
    "base_dir = './new_data_base'\n",
    "os.mkdir(base_dir)\n",
    "\n",
    "# Directories for our training,\n",
    "# validation and test splits\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Directory with our training cat pictures\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "os.mkdir(train_cats_dir)\n",
    "\n",
    "# Directory with our training dog pictures\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)\n",
    "\n",
    "# Directory with our validation cat pictures\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)\n",
    "\n",
    "# Directory with our validation dog pictures\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)\n",
    "\n",
    "# Directory with our validation cat pictures\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)\n",
    "\n",
    "# Directory with our validation dog pictures\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy first 1000 cat images to train_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy next 500 cat images to validation_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy next 500 cat images to test_cats_dir\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy first 1000 dog images to train_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy next 500 dog images to validation_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy next 500 dog images to test_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training dog images: 1000\n"
     ]
    }
   ],
   "source": [
    "print('total training dog images:', len(os.listdir(train_dogs_dir)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 31s 1us/step\n",
      "58900480/58889256 [==============================] - 31s 1us/step\n",
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 150, 150, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 150, 150, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 150, 150, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 75, 75, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 75, 75, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 75, 75, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 37, 37, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 37, 37, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 37, 37, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 18, 18, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 18, 18, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 18, 18, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 9, 9, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 9, 9, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                  include_top=False, \n",
    "                  input_shape=(150, 150, 3))\n",
    "\n",
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    \n",
    "    generator = datagen.flow_from_directory(\n",
    "        directory,\n",
    "        target_size=(150, 150),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='binary')\n",
    "    \n",
    "    i = 0\n",
    "    \n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        \n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        \n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            # Note that since generators yield data indefinitely in a loop, \n",
    "            # we must `break` after every image has been seen once.\n",
    "            break\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels = extract_features(train_dir, 2000)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 1000)\n",
    "test_feature, test_labels = extract_features(test_dir, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flattening our input data for dense layers\n",
    "train_features = np.reshape(train_features, (2000, 4 * 4 * 512))\n",
    "validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))\n",
    "test_feature = np.reshape(test_feature, (1000, 4 * 4 * 512))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 256)               2097408   \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,097,665\n",
      "Trainable params: 2,097,665\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Dense(256, activation=\"relu\", input_dim = 4 * 4 * 512))\n",
    "\n",
    "# adding Dropout layer for regularization\n",
    "model.add(layers.Dropout(0.5))\n",
    "\n",
    "model.add(layers.Dense(1, activation=\"sigmoid\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\keras\\optimizer_v2\\rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super(RMSprop, self).__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import optimizers\n",
    "\n",
    "model.compile(\n",
    "    loss=\"binary_crossentropy\", \n",
    "    optimizer=optimizers.RMSprop(lr=2e-5), \n",
    "    metrics=[\"acc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 4s 39ms/step - loss: 0.6209 - acc: 0.6535 - val_loss: 0.4484 - val_acc: 0.8450\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 4s 38ms/step - loss: 0.4443 - acc: 0.7975 - val_loss: 0.3640 - val_acc: 0.8700\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 4s 41ms/step - loss: 0.3641 - acc: 0.8430 - val_loss: 0.3241 - val_acc: 0.8790\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.3177 - acc: 0.8710 - val_loss: 0.3027 - val_acc: 0.8820\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 4s 41ms/step - loss: 0.2904 - acc: 0.8860 - val_loss: 0.2935 - val_acc: 0.8780\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 4s 39ms/step - loss: 0.2740 - acc: 0.8860 - val_loss: 0.2725 - val_acc: 0.8990\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 4s 37ms/step - loss: 0.2470 - acc: 0.9090 - val_loss: 0.2640 - val_acc: 0.9010\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 4s 38ms/step - loss: 0.2379 - acc: 0.9085 - val_loss: 0.2578 - val_acc: 0.8960\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.2222 - acc: 0.9195 - val_loss: 0.2562 - val_acc: 0.8980\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.2105 - acc: 0.9270 - val_loss: 0.2527 - val_acc: 0.9000\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.2056 - acc: 0.9225 - val_loss: 0.2466 - val_acc: 0.9030\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 4s 38ms/step - loss: 0.1927 - acc: 0.9230 - val_loss: 0.2428 - val_acc: 0.9080\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 4s 39ms/step - loss: 0.1829 - acc: 0.9340 - val_loss: 0.2413 - val_acc: 0.9060\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 4s 39ms/step - loss: 0.1720 - acc: 0.9365 - val_loss: 0.2384 - val_acc: 0.9110\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 5s 46ms/step - loss: 0.1653 - acc: 0.9435 - val_loss: 0.2372 - val_acc: 0.9090\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 3s 35ms/step - loss: 0.1634 - acc: 0.9395 - val_loss: 0.2399 - val_acc: 0.9040\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 3s 35ms/step - loss: 0.1552 - acc: 0.9475 - val_loss: 0.2358 - val_acc: 0.9090\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 4s 36ms/step - loss: 0.1463 - acc: 0.9490 - val_loss: 0.2357 - val_acc: 0.9100\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 4s 38ms/step - loss: 0.1401 - acc: 0.9505 - val_loss: 0.2342 - val_acc: 0.9090\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 4s 40ms/step - loss: 0.1359 - acc: 0.9515 - val_loss: 0.2363 - val_acc: 0.9090\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 4s 39ms/step - loss: 0.1329 - acc: 0.9520 - val_loss: 0.2323 - val_acc: 0.9070\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 4s 42ms/step - loss: 0.1226 - acc: 0.9600 - val_loss: 0.2434 - val_acc: 0.9040\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 4s 41ms/step - loss: 0.1190 - acc: 0.9615 - val_loss: 0.2328 - val_acc: 0.9070\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 3s 34ms/step - loss: 0.1148 - acc: 0.9630 - val_loss: 0.2334 - val_acc: 0.9090\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 3s 33ms/step - loss: 0.1072 - acc: 0.9640 - val_loss: 0.2348 - val_acc: 0.9050\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 3s 33ms/step - loss: 0.1106 - acc: 0.9600 - val_loss: 0.2362 - val_acc: 0.9060\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 3s 34ms/step - loss: 0.1074 - acc: 0.9675 - val_loss: 0.2360 - val_acc: 0.9050\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 3s 35ms/step - loss: 0.0945 - acc: 0.9715 - val_loss: 0.2368 - val_acc: 0.9050\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 3s 34ms/step - loss: 0.0969 - acc: 0.9695 - val_loss: 0.2386 - val_acc: 0.9080\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 4s 35ms/step - loss: 0.0934 - acc: 0.9690 - val_loss: 0.2483 - val_acc: 0.9010\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_features, train_labels, \n",
    "    epochs=30, \n",
    "    batch_size=20,\n",
    "    validation_data=(validation_features, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"./cats_and_dogs_vgg16_my.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc') \n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy') \n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss') \n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss') \n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 4ms/step - loss: 0.2483 - acc: 0.9010\n",
      "[0.24826842546463013, 0.9010000228881836]\n"
     ]
    }
   ],
   "source": [
    "accuracy_train = model.evaluate(validation_features, validation_labels)\n",
    "print(accuracy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_x = model.predict(test_feature)\n",
    "#print(type(predict_x), predict_x.shape)\n",
    "#np.set_printoptions(precision=3, suppress=True)\n",
    "#print(predict_x)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "len = range(0, 51)\n",
    "plt.plot(len, predict_x[:51], label='test') \n",
    "plt.scatter(len, test_labels[:51], color='r',label='label') \n",
    "plt.title('test result')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'models'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\DENGXI~1\\AppData\\Local\\Temp/ipykernel_65468/3983994627.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmodel2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./cats_and_dogs_vgg16_my.h5\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpredict_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_feature\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'models'"
     ]
    }
   ],
   "source": [
    "\n",
    "model2 = models.load_model(\"./cats_and_dogs_vgg16_my.h5\")\n",
    "predict_x = model2.predict(test_feature)\n",
    "print(predict_x)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a8f61be024eba58adef938c9aa1e29e02cb3dece83a5348b1a2dafd16a070453"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
